Temporal-influenced geospatial modeling system and method

ABSTRACT

A computer-implemented geospatial and temporal data storage system and method provides access to geospatial and temporal data as it is associated with events and event-types, thereby assisting in forecasting occurrences of identifiable events and/or results based on signature and/or pattern matching.

REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part application of U.S.non-provisional patent application Ser. No. 11/098,510, filed Apr. 4,2005 now U.S. Pat. No. 7,346,597 and entitled “Method and System forEvent and Result Prediction based on Geospatial Modeling”, thedisclosure of which is incorporated herein by reference.

TECHNICAL FIELD

The present invention relates to spatial modeling, and more particularlyprovides a computer-implemented geospatial and temporal data storagesystem associated with events and event-types that assists inforecasting occurrences of identifiable events and/or results based onsignature and/or pattern matching.

BACKGROUND ART

Geospatial modeling offers an approach to solutions to a variety ofcorporate, governmental and individual problems. For example, when a lawenforcement or fire department agency seeks to analyze or reactefficiently to crimes or fires, respectively, geospatial modeling mightbe used to locate the nearest water sources to quell the fire, or tolocate the nearest police stations for personnel dispatch. As anotherexample, when a retail chain seeks a location to open a new store,geospatial modeling might be used to determine most viable locationbased on available demographic information.

Geospatial information modeling has increased the ability to forecastevents, threats and results, as described, for example, in U.S.Application Publication No. 2005/0222829. However, it is often desirableto not only forecast or predict behavioral outcomes, but to influencethem as well. No previous systems are known which can model informationgeospatially so as to assist in spatial behavior modification, e.g.,influencing human behavior. No previous systems use geospatial modelingas disclosed by the present invention to assess not only where anarsonist might act next, for example, but how to influence a known, butnot captured, arsonist to attempt arson in a specific geospatial andeven temporal environment (i.e., at a specific place and time).

Typical of past systems is to predict a location of a future occurrenceof a given incident-type by simply analyzing the location of the pastsimilar incidents. Further, past systems are limited in that they do notsimultaneously allow for rapid assessment determinations with increasedaccuracy. Further, past systems make no effort to account for temporalaspects of events and event-types as they relate to the geospatialenvironment.

DISCLOSURE OF INVENTION

The present invention, in part, considers geographical features andmultiple types of measurements connecting past incidents to thosefeatures as part of an overall system and method for rapidly andaccurately assessing likelihoods of future events or results. Thepresent invention builds upon the assessed likelihoods through aninfluence element for analyzing most relevant variables, assessing theirindividual and combined abilities to influence the event or result type,and initiating a real-world response based on the assessment in order toincrease the chances of positively affecting or influencing real-worldbehavior. In one embodiment, the present invention considers past dataassociated with several event-types in order to arrive at an assessment.The real-world response can be a computer simulation offering optionsfor a real-world response, a report offering suggestions with cost/timeanalysis, or a physical or informational action resulting/communicateddirectly from the assessment, for example.

The present invention assists in forecasting occurrences of identifiableevents and/or results based on signature and/or pattern matching. In oneembodiment, the present invention identifies and indexes functionalmeasurements for one or more “cells” within a boundary or geographicalarea of interest, derives a signature pattern for event types ofinterest, and then links the derived signature to the stored cellinformation to assess the likely area for a future event of the same orsimilar event-type occurring. The present invention provides highlyrefined modeling processes to assist in quickly focusing on the propermeasurement type and/or variable type, and detailing analysis around themost relevant factors. In this way, the present invention allows formore rapid and more accurate assessment determinations. The presentinvention, in one embodiment, provides a decision support system forassisting in the determination of possible real-world factor influenceopportunities (e.g., moving highway check points, traffic routingpatterns, subway train departure timing) in order to influence desiredbehavior. The present invention, in another embodiment, provides acentralized portal capable of receiving geospatial as well as temporaldata input, in addition to problem definitions which can be assessedbased on stored geospatial and/or temporal data. Various user interfacesare provided in this embodiment for allowing a user to vary his or herlevel of involvement in selecting and inputting parameters for theanalysis.

A method according to the present invention can include the steps of:storing geospatial boundary information for one or more areas ofinterest; storing geospatial characteristic information pertaining to atleast one variable of interest in the form of one or more variablelayers associated with the one or more areas of interest; establishing ageospatial boundary pertaining to a first area of interest and a gridcontaining a plurality of cells within the boundary; identifying afunctional measurement of a cell element for each cell to the at leastone variable of interest for each of the one or more layers associatedwith the first area of interest, and indexing the functional measurementfor each cell; receiving geospatial information related to one or morepast events of at least one event type, including location informationfor the one or more past events; determining a likelihood associatingthe event type's relative proximity to the modifiable variable ofinterest; determining whether the modifiable variable of interest iscapable of influencing the event type; and upon determining that themodifiable variable of interest is capable of influencing the eventtype, influencing (or providing at least one suggestion for) areal-world modification to the variable of interest.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 shows a block diagram illustrating the interaction of severalcomponents of one embodiment of the present invention.

FIG. 2 is a block flow diagram illustrating steps taken in accordancewith a forecasting method of one embodiment of the present invention.

FIG. 3 is a sample diagram of an area of geographical interest inaccordance with an illustrative embodiment of the present invention.

FIG. 4 shows the diagram of FIG. 3 with a grid overlay.

FIG. 5 shows a close-up segment of the diagram of FIG. 4 as anillustration of determining cell measurements in accordance with oneembodiment of the present invention.

FIG. 6 shows the sample diagram of FIG. 3 with points illustrating pastevent data in accordance with an illustrative example of employing thepresent invention as described in the specification.

FIG. 7 is an example probability density function in accordance with anillustrative example of employing the present invention as described inthe specification.

FIGS. 8 through 11 are example diagrams showing graphical “hot spot”representations according to various embodiments of the presentinvention.

FIG. 12 is a schematic diagram showing an arrangement of componentsaccording to an alternative embodiment of the present invention.

FIG. 13A is a schematic diagram showing additional componentsinteracting with the event likelihood component according to variousembodiments of the present invention.

FIGS. 13B through 18 show sample representations of a geographicalboundary and elements employed in providing an assessment in accordancewith one embodiment of the present invention.

FIG. 19 is a depiction of a pair of probability density functions inpartially overlapping relation in accordance with one aspect of thepresent invention.

FIG. 20 shows a series of images of varying resolution in accordancewith a further aspect of the present invention.

FIGS. 21 through 23 and 25 are graphical depictions of geographicalareas of interest as described in connection with an exampleimplementation of the present invention.

FIG. 24A is an example histogram and FIG. 24B is an example probabilitydensity function in accordance with an example implementation of thepresent invention.

FIG. 26 is a sample graph illustrating an example probabilitydistribution curve.

FIG. 27 is a flow chart illustrating a method in accordance with thespatial behavior modification aspects of the present invention.

FIGS. 28 a through 28 e are sample diagrams illustrating temporal dataindexes that may be used in accordance with one aspect of the presentinvention.

MODES FOR CARRYING OUT THE INVENTION

As shown in FIG. 1, the present invention provides a system 10 includinga boundary component 12 which allows the system or a user to set forthor incorporate a geospatial boundary to be analyzed in accordance withthe present invention. The boundary component also can specifyindividual cells within the boundary. Cells can be provided in a gridoverlay such as shown in FIG. 4, with each cell being a regular andsquare-shaped element in a square- or rectangular-shaped grid.Alternatively, the cells can be provided in irregular shapes, such asshown in FIG. 13B, where as an example the cell boundaries are definedas the county boundaries in Maryland. Such irregular shapes can bedictated by political boundaries, natural boundaries, system- oruser-created boundaries, or randomly. In one embodiment, boundaryinformation and cell information can be stored in database 14 for one ormore areas of interest.

The layer component 16 allows the system or a user to specify orincorporate one or more layers of geospatial features or characteristicspertaining to at least one variable of interest. For example, a “roads”layer may be provided having information pertaining to roads within thedefined geospatial boundary. The roads layer may also be provided withadditional variables of interest associated with roads, such as thenumber of lanes in a given road, whether the road is a highway or a citystreet, or whether the road is one-way or two-way, for example. Exampletypes of layers can include: roads, cities, towns, cemeteries,embassies, gardens, industrial facilities, junctions, educationalfacilities, bodies of water, settlements, national parks, city or countyfacilities, bridges, hotels, fuel stations, hospitals, airports, trainstations, parking lots, campsites, rest areas, archeological sites, andchurches/holy places. Other layers can include information such asdemographic information such as age, gender, income, and/or religiontype, for example. It will be appreciated that the present invention canincorporate both static (e.g., bridges) and non-static (e.g., roadconstruction locations, police speed traps, etc.) variables. Layer andvariable data are stored in spatial database 14. While boundarycomponent 12 and layer component 16 are both shown in FIG. 1 asproviding information to the same database 14, it will be appreciatedthat database 14 can be divided as necessary into multiple databases inorder to accommodate the most suitable database architecture for a givensystem application, including temporal data as described more completelyhereafter.

A function component 20 provides programming for identifying andmeasuring a functional measurement associated with an element orelements of each cell. For each cell, the function component can helpdetermine a cell element from which measurements can be taken, asnecessary. As shown in the regular cell example of FIG. 5, eachsquare-shaped cell 44 has a middle point 54 derived from the knowncenter of a square (i.e., the intersection of a vertical line drawn atthe width halfway point with a horizontal line drawn at the lengthhalfway point). As shown in the irregular cell example of FIG. 13B, eachirregular cell can have a single cell element located at the center ofmass or centroid of the cell (see points 302). Alternatively, theirregular-shaped cells can have multiple elements which assist inderiving more accurate values as described hereafter (see points 301).These multiple elements may be system-generated automatically, and mayalso be generated by the user or through some semi-automated process. Itwill be appreciated that high intra-cell measurement variability leadsto imprecise representation of the underlying statistical surface as achoropleth when a single cell element is used for measurement. Usingmultiple elements in each cell produces a more accurate representationof the statistical surface, and in the limit this leads to a dasymetricmap with perfect representation of the surface.

The function component can determine a measurement for each cell fromthe cell element (e.g., midpoint, centroid, etc.) to the variable ofinterest. In one embodiment, this measurement is the nearest neighbordistance. For example, as shown in FIG. 5, cell element is midpoint 54,the variable of interest can be airport 55, and the measured nearestneighbor value is distance 56. In another embodiment, this measurementis the nearest neighbor value. For example, as shown in FIG. 5, cellelement is midpoint 54, the variable of interest is the number of laneson the nearest road 57, and the nearest neighbor value is two (assumingroad 57 has two lanes). In another embodiment, this measurement can bethe density or concentration of a particular item. In still a furtherembodiment, this measurement is the average distance by actual path. Forexample, as shown in FIG. 5, actual path 59 can take into account thedistance using actual roads from the cell element 58 to the variable ofinterest (e.g., a bridge crossing). In one embodiment, actual path canbe considered based on artificial paths (e.g., man-made roads) as wellas based on natural paths (e.g., a river or a clearing). In a furtherembodiment, the determined measurement is the Manhattan distance. Forexample, as shown in FIG. 5, cell element 63 can trace a Manhattandistance path 64 to variable of interest, and the distance along path 64can be determined.

In a further embodiment the determined measurement is based onvisibility. In this regard, the present invention employs a GIS functioncalled a “viewshed”, which gives the area visible along line of sightpaths from a single point on the ground. The way the invention uses thisis by including a ‘has line of sight’ calculation from an event to afeature. For example, if the events are animal poaching in a nationalpark, each event could have a variable calculation on whether it isvisible from a ranger tower in the park. This can be generalized toincluding the number of towers which are visible from each poachingevent.

In another embodiment, the determined measurement is based on any typeof function call to another computational function (e.g., a functionderived programmatically by a third party), such that the results can bereturned to the system for use in determining a signature. Thus, theinvention is not limited to a probability density function or data setsthat comprise a probability density function.

In yet a further example, the measurement is manually created, wherein auser establishes a signature using information not derived from theinvention. For example, the invention can allow the user to input via auser interface a generalized density function manually. In anillustrative embodiment, this function can take the form of a histogram,a continuous function of distance, or any values the user may require.The system will then validate and make scaling changes as necessary toensure the probability density function is valid. For example, if theuser knows that of the store locations he/she owns, 20% are two story,30% are three story, and 50% are one story dwelling, a probabilitydensity function for number of floors in a store that looks likeFunction A below:

This function can be drawn on a GUI tool, entered into a table, ordescribed with a function, for example.

Once all measurements are determined and calculations performed, thefunction component stores all measurements and calculations for lateruse when examining signature information associated with actual trainingdata.

In one embodiment, layer component 16 includes an update layer elementwhich operates to update the spatial database 14 upon receiving changesto existing layers or entirely new layers. The update layer element cantrigger the layer component 16 to notify the function component 20 uponreceipt of the updated or new layer, at which point the functioncomponent can either complete whatever current processing is occurring,or the function component can delay any further processing until theupdated or new layer is incorporated. To the extent the new or updatedlayer is part of the currently processing assessment, the functioncomponent can re-initiate this segment of the analysis.

An event likelihood component (ELC) 25 performs analyses based onsignatures constructed from available actual data received, for example,from an input component 30, to determine likelihood of similar eventsoccurring in the geospatial boundary. The event data can be, forexample, locations where previous armed robberies occurred. A signaturederivation component 23 receives the data, and measures and analyzes itagainst one or more of the layers entered in database for a givengeospatial boundary. The signature derivation component 23 thenconstructs a raw signature, reduces the information into a histogram orprobability density function (see, e.g., FIG. 7), and establishes asignature for this event type (e.g., armed robberies) within thegeospatial boundary. The ELC 25 receives the derived signature from thesignature derivation component 23, then combines the signature with thefunctional measurements stored by the function component regarding eachcell, and thereby measures a level of signature match with one or morecells for the given event type. The level of signature match is anassessment 27 which can be determined by calculating a score associatedwith each cell. The scores can be plotted on a choropleth graph as shownin FIGS. 8 through 11, which can give a viewer a “hot spot” typereading, for example.

It will be appreciated that components 12, 16, 20, 23, 25 and 30 can beinterconnected in a variety of configurations, such as by local areanetwork, or wide area network such as the Internet, for example. Eachcomponent may comprise an individual server having a processor, memoryand storage, or may comprise a programming element of multiple programsstored and executed on a single server, for example, as is known in theart. In one embodiment, various computationally intense aspects of theinvention are distributed among multiple processors to promoteefficiency and speed of the present invention.

A method for employing the system of the present invention to arrive ata forecast or an assessment of a future event likelihood can occur asshown in FIG. 2, and a graphical representation of some of the stepsdescribed can appear as shown in FIGS. 3 through 11. As shown at 31 inFIG. 2, and 40 in FIG. 3, a geospatial boundary can be defined, such asa 20 mile by 20 mile square area around Washington, D.C. Within thisboundary, a grid 42 of smaller geographical areas (i.e., cells 44) canbe created within the boundary, as shown in FIG. 4. As shown within theboundary, one or more layers having “variables of interest” (e.g.,schools, roads, rivers, convenience stores, etc.) can be established.These layers can be thematic data sources and the establishment andinput of one or more of these layers corresponds to step 32 in FIG. 2.In the particular example of FIG. 5, an airport (Reagan NationalAirport) is shown as at 55, along with some rivers 53 and roads 57.

Next, proximity or functional measurements are derived and stored foreach cell and for each variable of interest, as indicated at step 33.For example, as part of the method of the present invention, for eachcell 44, a proximity measure can be determined for each of the differentvariables of interest. Using cell 44 in FIG. 5 as an example, there is astraight line proximity measure (proximity SL) 56 between the midpointor training point 55 of cell 44 and the airport 55. A proximity (ornearest neighbor) SL measurement can be stored for each cell againsteach variable of interest. It will be appreciated that the presentinvention contemplates straight line proximity measurements as well asalternative proximity measurements, such as proximity by road traversal,proximity by time of travel, proximity by time of travel using motorizedvehicle, by an adult or child walking, by an adult or child running, andso forth.

The invention further contemplates functional measurements such asdescribed above to accommodate non-proximity or non-nearest neighborevaluations. Other factors which can be considered in addition toproximity include natural measures, such as temperature, time, costsurface distance, elevation, wind speed, precipitation, tidalinformation, pressure, humidity, luminance or slope, for example. Stillother factors can include feature density, or demographics such asethnic populations and population density. These types of factors can beconsidered “continuous” factors, to be described more completelyelsewhere herein. As opposed to continuous factors, the presentinvention can also consider “discrete” factors or variables, such ascertain demographics like predominant religion or predominant ethnicgroup, for example. Other discrete factors the present invention canconsider include land utilization, zoning and/or predominant vegetation.

As a specific example, cost surface distance considers the actual orprojected cost of traversing a distance. For example, traversing asection of white water rapids may be most directly possible (i.e.,involving the shortest actual distance) by boarding a gondola or similarvehicle, but if the vehicle ride incurs greater financial costs than ifone were to traverse a downstream bridge, then the cost surface distanceof the vehicle ride would be greater than the cost distance to take thedownstream bridge. In one aspect of the invention, a nearest neighbordistance (NND) cost surface factor type function (FTF) calculates thedistance to a feature according to a cost surface. The inputs to thisFTF are a feature layer (e.g., churches, shopping centers, railstations) and a raster layer that models the “friction” associated tothe travel on the feature layers. The friction layer can be modeled frominput data such as terrain, transportation, weather and financial cost,for example. The friction surface in turn provides a field where thepath of least resistance is calculated between a location and a specificdestination (the feature data). The result provided by the presentinvention is a cost FTF that permits the calculation of “distance” interms of non-Euclidean units (e.g., time and money). Such a resultreflects the perspectives of human nature and how they travel and arriveat access to goods and services most efficiently. In one embodiment ofthe present invention, any impassable location can be assigned anegative value in the cost surface. Thus, if an AOI grid point or eventpoint falls on an impassable cost cell or an unreachable island, forexample, the raw factor data (RFD) will receive a null value and thatpoint for the purposes of the specific factor will be excluded from theassessment entirely. It will be appreciated that, in one embodiment ofthe present invention, Geospatial Data Abstraction Library(GDAL)-supported raster files can be input as AOI grid values through animport procedure as is known in the art. Other valid cost surface inputformats can include, for example, ERDAS Imagine™, ESRI ASCII Grid andTIF formats, as well as other raster imagery formats.

Once each cell has been measured according to the appropriate factor forthe problem to be solved or event to be forecasted, the presentinvention can receive information pertaining to a location of ameaningful event or events (e.g., a robbery), as indicated at 34 in FIG.2. The location information can be specified by block and street (e.g.,4400 block of Hill St.), by latitude and longitude, or other knownformat. As shown in FIG. 6, events can be designated by black dots 60.

Next, the invention can identify the proximity of the event to thevariables of interest (e.g., the robbery occurred 0.2 miles from aconvenience store, 0.5 miles from a highway, and 2 miles from a river).Based on the identified location, proximity to variables of interest canbe determined, much like was done for each cell.

Next, the invention can establish a “raw signature” for the event. Forexample, if five events (robberies) occur and there are two variables(highway, river), the raw signature might look like that in Table 1below:

TABLE 1 Event # Variable # Calculation Special/Feature ID 1 1 (highway)0.2 km 55 2 1 0.1 km 55 3 1 1 km 443 4 1 0.7 km 618 5 1 0.15 km 99 1 2(river) 4 km 12 2 2 2 km 12 3 2 7 km 12 4 2 1.2 km 12 5 2 5 km 12

It will be appreciated that the special/feature ID shown in column 4 canbe an identifier for the highway (e.g., 55 can be Rte. 95, 443 can beRte. 66, etc.) or other variable.

Next, the invention can measure a probability density function for eachvariable, so as to have a probability associating the events with avariable of interest, as shown by the example graphical representation70 in FIG. 7. In this example, the five data points from item 7represent the distance of the five robbery events from a highway. Inthree cases, the robbery was very close to a highway. One case wassomewhat close, and another case was more distant from the highway. Theprobability density function is performed for all events against eachvariable individually. While the representation in FIG. 7 is acontinuous probability density function, it will be appreciated thathistogram-type probability density functions can be provided inconnection with discrete variables, such as those identified above(e.g., predominant religion or ethnic group demographics, landutilization, zoning, predominant vegetation, etc.).

Once this is done, a refined signature based on the probability densityfunction can be established. In one embodiment of the invention, theprobability density functions can be converted into a binary file, whichcan then be used in each of the cells outlined above. The processing ofthe event data from input to a signature is illustrated at step 35 inFIG. 2, for example. At step 36, the event signature is compared withthe cell signatures previously determined and stored at step 33.

Next, for each of the cells, a score indicative of that cell'scompatibility with the refined signature can be determined, as at 37 inFIG. 2. Each cell will have a probability score associated with eachvariable. In one case, the total score can be the sum of each of theprobability scores. In other cases, coefficients can be provided toassign weights to different variables as described hereafter.

Once the cells have been given a score, the entire boundary 80 can beviewed at a distance to determine geospatial “hot spots” 82, as shown inFIGS. 8 through 11, and indicated at step 38 in FIG. 2. For instance,instead of limiting analysis to particular cells, the entire region canbe analyzed for groups of cells that appear to have high probabilitiesof an event occurring. FIGS. 9 and 11 show the grid 80 without originalcell lines, and FIG. 10 shows the cells slightly faded to reveal roadsunderneath.

In this way, the present invention helps evaluate where a future similarevent might occur. Quite often, hotspots are revealed where no priorevent has occurred. The system and method of the present invention canthus reveal that the environment in this hotspot is similar to theenvironment where prior events or results had occurred.

As shown in FIG. 12, in one embodiment of the invention, the eventlikelihood component 25 interacts with additional components to assistin rapidly and efficiently reaching the most optimal assessment. Forexample, a cell comparison component 91 determines, for each past eventin the training data, the individual cell element nearest to that event,and then associates the functional measurement for the nearestindividual cell element with the data event. In doing so, the presentinvention need not determine a functional measurement for the event whenit can be looked up by cell index, thereby improving the speed of thesignature derivation. The cell comparison component 91 can then providethis information to the event likelihood component 25, which determinesthe level of signature match as described above.

As further shown in FIG. 12, the event likelihood component 25 caninteract with a signature transfer component 92, which allows the systemor a user to take event signatures (e.g., armed robbery signature) andapply it to a different area of interest as held or entered into theboundary component database 14. In this way, the invention contemplatesthat events which occur in a separate geographic region (e.g., bankrobberies in Richmond, Va.) can be used to determine or predict similarevents in another region. The signature transfer component can operateby taking the signatures and/or associated collections of probabilityfunctions for given event types in a first region, and applying themwithin the second region. This can occur via functional component 20which accesses a separate set of data from boundary component database14, or via a separate functional component 200 which has beenpre-established for the second region.

As part of signature transfer component 92, the present inventioncontemplates that there may be variables of interest associated with afirst region which have no direct equivalent in the second region. Forexample, “sidewalks” may be a variable of interest in the first region,but if there are no sidewalks in the second region, this variable couldnot ordinarily be taken into consideration in determining an eventsignature. In such cases, the present invention provides the signaturetransfer component with a feature substitution or variable equivalencecomponent 93, which determines the next most likely substitute for theintended variable. For example, if there are no sidewalks in region 2,the feature substitution component may employ residential, secondary ortertiary roads as a substitute, given that such roads typically runparallel to sidewalks.

Further in connection with event likelihood component 25, a non-pointdata evaluation component 94 can be provided for situations where thepast event training data is not conducive to representation in pointformat. For example, a location where an armed robbery occurs can be apoint, such as the location of the bank where the robbery occurred.However, if the event type is broadened to encompass, for example, aradius of where an alleged perpetrator may have fled to within twominutes of the robbery, the past event data would not be represented asa point, but rather an area. The present invention can accommodate suchpast event data via the non-point data evaluation component 94. As such,this component 94 considers variations of non-point data such as, forexample, polygons, lines, and three-dimensional data.

As further shown in FIG. 12, an influence component 95 can also beprovided which assigns variable weights to variables of interest. In oneembodiment of the present invention, the influence component 95determines relative weights to associate with each of the variables ofinterest based on comparing the established signature for each variableof interest with a control signature. The control signature can be, forexample, a random sample of data. In doing so, the present invention candetect the amount of overlap between the two signatures as a measure ofthe power of the given variable of interest in distinguishing the eventlocations from random locations.

The influence component can consider differences in variance betweeneach of the established signatures and the control signature indetermining relative weights. Further, the influence component canconsider differences in mean between each of the established signaturesand the control signature. It will be appreciated that while the presentinvention can distinguish between differences in variance as well asmean, a simple t-test or similar test does not do this.

The influence component can also consider differences in directionalitybetween each of the established signatures and the control signature. Asshown in FIG. 19, the directionality of the difference is determined bythe directed divergence. This directionality determines whether eventsare closer or farther than expected statistically, which translates intothe variable classifications. Once determined, the relative weights foreach variable can be incorporated as variable coefficients in subsequentcalculations by the function component, and in cases where a variableappears to have no determinative effect, the variable can be assigned acoefficient of zero.

As part of influence component, the present invention can determine alikelihood associating the event type's relative proximity to amodifiable variable of interest, i.e., a variable that can be modifiedin the real world (e.g., a traffic light that can have its timingadjusted, a roadside inspection point that can be moved, a parking lotentrance that can be closed). The influence component can also determinewhether the modifiable variable of interest is capable of influencingthe event type being studied (e.g., will changing parking entrances haveany effect on vehicle break-ins?). Upon determining that the modifiablevariable of interest is capable of influencing the event type, thepresent invention can influence (or provide at least one suggestion for)a real-world modification to the variable of interest. For example, thepresent invention can determine that certain criminal behaviors areexhibited near stop signs. The present invention can make arecommendation to install or move one or more stop signs to an area thatmay be monitored by law enforcement or to an area with fewer targetscapable of harm. The real-world modification can be initiated bycontacting relevant personnel by telephone or electronically throughknown communication means. The real-world modification can also beinitiated by sending control signals to various physical machines anddevices with instructions suitable for carrying out a physical action.For example, the influence component can signal a traffic system toinitiate longer yellow (or amber) light periods, or the influencecomponent can direct a fuel line transfer system to shut down. It willbe appreciated that appropriate human oversight and approval structures(e.g., electronic approval) can be implemented in accordance with safetyand other factors governing any application of this aspect of thepresent invention. Constraints may also be placed on the ability of thepresent invention to initiate real-world modifications. For example, apower system shut-down can be constrained by the need to power essentialoperating equipment. As another example, e-mail notifications topersonnel may be constrained by time of day and day of weekrequirements. The real-world modification initiation can occur withinthe entire AOI or within only a portion of the AOI.

In considering the impact of specific factors in any assessment, thepresent invention can employ a tool and provide programming for factormetric analysis. Such analysis can be used to determine the “rank order”significance of factors, and how they contribute to assessment values.In one embodiment of the present invention, factor metric analysis canbe applied in four different ways: (1) an overall metric analysis, byviewing metrics over the entire AOI grid, where the analysis toolreports the “average” factor contribution for the entire assessmentarea; (2) a regional metric analysis, by viewing metrics over a regionof the AOI in which some threshold has been established (e.g., the top10% of the assessment); (3) a point metric analysis, by viewing metricsat a specific cell; or (4) an event metric analysis, by viewing metricsat each of the grid cells where an event existed or occurred. Athreshold would typically be applied in the overall or regional metricanalysis in order to reduce computation and overburdening the system ofthe present invention.

In one embodiment of the present invention, five factor metric areemployed to characterize the relationship between factors andassessments that result from use of the present invention. First, thelikelihood metric refers to the number corresponding to the raw factordata (RFD) value for a factor's event probability distribution curve.The likelihood value is independent of a factor's weight. For example,in FIG. 26, the RFD value 300 is assigned the likelihood of 40%. Asecond factor metric is weight, which can be applied to emphasize orde-emphasize the importance of a factor as described above and inconnection with FIG. 12, for example. Factor weights can be created on acase-by-case basis, or computed in batch by the present invention usinga command that will integrate the assessed signature, for example. Inone embodiment, each factor has a default weight of one (1.0), meaningit is of equal significance with all other factors. A third factormetric is weighted likelihood. This is the likelihood metric multipliedby the weight for a given factor. Thus, for example, if there is a 40%likelihood that the distance for a particular event is 1.8 kilometersfrom a highway intersection, and the highway intersection is a keydeterminant from past testing such that it is given a weight of 2.0,then the weighted likelihood is 80 percent (2.0 times 40%).

A fourth factor metric is contribution. Contribution is calculated bythe present invention by subtracting the global minimum value of anassessment layer from the weighted likelihood value of a factor, whichcan effectively de-emphasize factors that do not have significantrelative variance in the assessment layers. For example, given severalfactors contributing to a model, each cell will be comprised of the sumof the effects of each factor. The cumulative effects of a factor overall cells is the contribution of that factor. The contributions arenormalized such that the sum of all factor contributions sum to one.

A fifth factor metric is contrast measure, which is the result of theintegration of an assessed signature. This factor metric illustrates thedifference between an environment probability density function (PDF)curve and an event PDF curve as in FIG. 19. The closer the value of thismetric is to 1.0, the more dissimilar the two PDFs are, while the closerthe value of this metric is to 0.0, the more similar the PDFs are.Similar PDFs are interpreted to mean that the factor does not providecontrast with the background, and therefore does not provide predictivepower to the model. The contrast measure can be used directly as aweight on the influence of each factor on the result, therebydeemphasizing unproductive factors.

In the embodiment of the invention wherein a number of layers areimposed upon the boundary, each of which is indicative of geospatialcharacteristics of at least one modifiable variable of interest, thelikelihood determination step is applied to each of the modifiablevariables of interest, and real-world modifications can be influenced(or suggested) on a plurality of the variables of interest. It will beappreciated that influencing a real-world modification to the variableof interest can be restricted by one or more constraints, as indicatedabove. It will further be appreciated that the influencing of areal-world modification to the variable of interest can be performedwithin only a portion of the AOI, as indicated above. In one embodimentof the present invention, computer simulations are used to determinewhether the modifiable variable of interest is capable of influencingthe event type. In another embodiment of the present invention, arelative weight can be assigned to the variable of interest based uponthe step of determining whether the modifiable variable of interest iscapable of influencing the event type.

In a further embodiment of the present invention, an objective functionis derived and solved to determine one or more optimal modifications forthe variable of interest so as to minimize or maximize the likelihood ofthe future event occurring in or away from the specified geospatialarea. One option used by the present invention to solve the objectivefunction is a naïve algorithm, including, for example, a Monte Carloalgorithm, as described in more detail below. Another option used by thepresent invention to solve the objective function is an evolutionarybased algorithm, also described in more detail below.

It will be appreciated that the event being analyzed can be an activity,a behavior, a customer set, a threat. It will be appreciated that thevariable of interest can be influenced at a given time. It will furtherbe appreciated that artificially influencing the at least one variablecan involve an informational event (e.g., newscast, video, messagereleases from high altitude, etc.). It will further be appreciated thatartificially influencing the at least one variable can involve aphysical process.

In one aspect of the present invention, the influence component canoperate so as to selectively increase or decrease the likelihood of afuture event occurring in a specified geospatial area. For instance,programming can be provided as outlined above for making probabilityassessments of a given event-type for a given geospatial area ofinterest (AOI) based on the geospatial characteristics of at least onevariable of interest (VOI) and past geospatial data for the event-type.The programming can further operate to determine whether the at leastone VOI is modifiable and causal of the event-type. Upon determiningthat the at least one VOI is modifiable and causal of the event-type,the programming can iterate and modify a quantitative characteristic ofthe VOI until the desired likelihood of the event type is obtained.

The step of modifying a quantitative characteristic of the at least oneVOI can include modifying a weight factor applied to the VOI. The stepof modifying a quantitative characteristic of the at least one VOI canalso include increasing or decreasing the number of instances of the VOIwithin the AOI. Further, the step of modifying a quantitativecharacteristic of the at least one VOI can be performed withinpre-established constraints on the VOI.

It will be appreciated that the programming is capable of making aprobability assessment based on the geospatial characteristics of aplurality of variables of interest (VOIs), and the step of iterativelyexecuting the programming and modifying a quantitative characteristiccan be applied to a plurality of VOIs that are modifiable and causal ofthe event-type.

In this way, the present invention can signify the importance of variousmodifiable features. Using such feature space modeling, the mostimportant features or variables are identified as such. An example flowchart illustrating the process steps is shown in FIG. 27. As shown inFIG. 27, a given parameter is first identified as at 310. If theparameter is not modifiable as determined at 312, then another parameteris identified back at 310. If the parameter is modifiable (e.g., aroadside inspection point that can be moved), then a determination ismade as at 314 as to whether the parameter is causal. For example, if ithad been determined that there was some level of causality betweenroadside inspection points and bus bombings, then there would be apositive determination of causality for stop signs. If there is nocausality, then another parameter is identified back at 310. If there iscausality, even at some smaller level, then the parameter is stored as acausal parameter as at 316 and a weight can be assigned to the parameterbased upon the determined level of causality, also at step 316. In oneembodiment of the present invention, each relationship between aparameter or feature and an event/activity can be examined to determinewhether the parameter/feature is an attractor for the event type oractivity, a repellor, or a standoff (neither attractor or repellor,i.e., neutral). In a further embodiment of the present invention, pairsor other multiples of parameters/features are analyzed together todetermine their joint characterization as an attractor, repellor orneutral element. In a further embodiment of the present invention, acontrast measure weight can also be applied as at step 316.

Further in FIG. 27, once the parameter is assigned a weight, anassessment can be run by the system of the present invention as at 318.If a factor is determined to be irrelevant at this stage, for example,it can be removed from the assessment for future assessment runs. If aweight is determined to be too high or too low, it can be adjusted andanother assessment run. If the factor signature and/or PDF is notdetermined to be as expected (e.g., a strong attractor or repellor forthe event type) as at 320, then a determination is made as at 322 as towhether data was simulated. If not, then an assessment determinant(e.g., parameter weight) can be changed as at 324 and the assessmentre-run as at 318. If so, then the feature can be modified as at 326 tobe a stronger or weaker attractor or repellor, and then the assessmentcan be re-run as at 318. As an example,

Once the factor signature is as expected after an assessment run, thenthe present invention can modify an area of interest (AOI) or a portionthereof to reveal the effect on the behavior being examined, as at 328.Using the bus bombing example, roadside inspection points can be removedand/or inserted on different streets, to reveal if this has an affect onthe location, frequency, damage, casualties, etc., of bus bombings.Next, the present invention can be used to insert or develop constraintsfor a heuristic algorithm as at 330. For example, if roadside inspectionpoints must occur at least twice in a five mile span of highway, must bealongside a road, separated by x distance units and limited in overallnumber, then the present invention can operate to constrain the analysisand possible solutions. If it is determined as at 332 that additionalmodifiable parameters are required in order to better influence thedesired behavior, the present invention can return to step 310 for suchadditional review. If no new modifiable parameters are required, thepresent invention can optionally proceed to consider new locationswithin the AOI and determine which features to include as at 334. Thepresent invention can operate so as to propose real-world modificationsto features consistently throughout an entire AOI, or on a piecemealbasis. At step 336, the present invention is used to derive or toreceive an objective function having a goal of minimizing or maximizingthe risk and/or reward inside or outside of the AOI. The objectivefunction can be used to find the optimal, heuristic solution for whereand when to move/add/remove one or more features. At step 338, thepresent invention can use programming in the form of optimizationsoftware, for example, to solve the objective function and presentsolutions and/or recommendations for solutions. If the desiredbehavioral change is achieved as at step 340, then the present inventioncan proceed to analyze the results as at 344 and end the process. If thedesired behavioral change is not achieved, then at step 342 the presentinvention can optionally start over, make changes to the heuristicalgorithm, and/or make changes to the assessment.

It will be appreciated that the present invention considers naïve-basedand evolutionary based algorithms in solving the objective function. Oneexample of a naïve-based algorithm is an exhaustive approach thatdivides an AOI into a certain number of possible feature locations, anditerates through all possible combinations of placing features in thoselocations to find the best result. Such an approach can becomecomputationally expensive if more than just a few features are used,with the expected number of iterations being:

${\frac{n!}{{k\left( {n - k} \right)}!} + n},$

where n is the number of possible locations and k is the total number offeatures (e.g., checkpoints) that can be modified. As n becomesincreasingly large, the equation approaches n^(k). In this example, forone hundred locations and five features, there would be somewhere nearten billion iterations.

Another approach for a search algorithm used in one embodiment of thepresent invention is a Monte Carlo simulation. This algorithm takes arelatively large random sample from all possible combinations offeatures, and singles out the best solution(s). The algorithm ensuresthat each part of the AOI is sampled, and stops prematurely if a targetcritical value is found. The target critical value can be pre-determinedaccording to user input.

In another embodiment of the present invention, an evolutionaryalgorithm is employed that will converge to a better solution over eachiteration. For example, a genetic algorithm uses survival of the fittestprinciples in finding a suitable solution.

Assessments and analyses in connection with the behavior modificationaspects of the present invention can result in an electronic or printedreport outlining options for various real-world actions that might betaken to influence the desired or undesired behavior. Alternatively, acommunication can be sent to decision makers or action takers indicatingsuggested actions and possible risks/costs/rewards associated with each.Further, an actual informational or physical event can be implemented asa result of the analysis. For example, a communication can be sent fromthe system via alert component 96, for example, to a news organization,an Internet blog, or any other type of media to disseminate a messagedesigned to influence real world behavior. If, for instance, it isdetermined that more frequent suicide bombings occur when a foreigndignitary is arriving in town, then a message (possibly false, dependingon the circumstances) can be sent out to alert those who might act onsuch information that a foreign dignitary will be arriving.Additionally, a physical embodiment of a real-world action can beinitiated. For example, a traffic light may be caused to turn from greento yellow to red more frequently if it is determined that car bombersare more likely to strike when a traffic light has a longer red lightperiod. There are vast examples of different informational and physicalactions that can result from the analysis performed according to thepresent invention.

In a further aspect of the present invention, change detection forgenerated signatures is employed. Change detection is important so thatthe system keeps data and analyses fresh and current in order to bestinform related decisions. As an example, if the system of the presentinvention assesses locations of boating accidents and determines thatthey are much more likely to occur in shallow water near majormetropolitan cities as opposed to on the open water of a bay, lake orocean, then the developed boating incident signature would reflect suchanalysis. Using the present invention, the boating incident signaturecan be periodically reviewed to assess whether there are any changes tothe signature over time. For example, a reassessed signature mightdetermine that the boating accidents have changed in theirpredictability from location only, to additional factors such astemperature change and day of week (e.g., accidents occur morefrequently on Saturdays and on days where the temperature is 10 degreesor more warmer than the previous day).

In one embodiment of the present invention, change detection occurs onthe fly as each new event used in an assessment is applied. For example,a threshold change factor (e.g., 10%) for a change statistic (e.g., theaverage distance from the event to all previous events) can bepre-defined so that if the new event exceeds that threshold, a signaturechange is acknowledged, reported and recorded. The change statistic isthus calculated to determine whether the pre-determined threshold hasbeen met or exceeded. In one embodiment of the present invention, thechange statistic calculates the similarity of the known new event to allprevious events of the given event type. This method can be called theaverage link clustering method on the feature space vector model of thepresent invention.

In one embodiment of this aspect of the present invention, the signaturederivation component establishes and stores an original geospatialsignature for a given event type based on a determined likelihoodassociating the event type's relative proximity to a variable ofinterest (VOI) within a geospatial area of interest (AOI). An interfacereceives geospatial information pertaining to one or more subsequentevents of the given event type within the AOI, including locationinformation for the one or more subsequent events. Then, the eventlikelihood determinant component classifies the received information aspart of the original signature or as an outlier, and, upon the receivedinformation meeting or exceeding a pre-determined threshold forclassification as an outlier, the event likelihood determinant componentestablishes and stores a reassessed signature for the given event type.

In another embodiment of this aspect of the present invention, changesin techniques, tactics or procedures associated with an event aredetected. The signature derivation component establishes and stores anoriginal geospatial signature for a given event type based on adetermined likelihood associating the event type's relative proximity toa variable of interest within a geospatial area of interest (AOI),wherein the original signature can be depicted as a technique, tactic,procedure or combination thereof (e.g., bombing of fuel tanks in a warenvironment, channeling traffic using toll lanes, pass attempts overforty yards in a football game). The interface then receives geospatialinformation pertaining to one or more subsequent events of the givenevent type within the AOI, including location information for the one ormore subsequent events. The event likelihood determinant component thenestablishes and stores a reassessed signature for the given event typebased on the received geospatial information, compares the reassessedsignature to the original signature and depicts changes in thereassessed signature as a change in technique, tactic, procedure or acombination thereof.

In one embodiment of the present invention, event types can havesub-indicators. Thus, for example, if suicide bombings are anevent-type, there may be a sub-indicator for a Party1-type suicidebombing, another sub-indicator for a Party2-type suicide bombing and athird sub-indicator for a Party3-type suicide bombing. An originalgeospatial signature is stored for each sub-indicator and newly receivedinformation is classified as pertaining to one of the plurality ofsub-indicators.

In another embodiment of the present invention, a nonparametricextension of a two-tailed t-test can be employed for change detection.Classical statistics provides a two-tailed t-test for accepting orrejecting the null hypothesis that an observation is significantlydifferent from the mean (e.g., higher or lower). One can interpret thistest as a statistical basis for determining whether the data value wasproduced by the same (or similar) distribution. This test is extended inthis embodiment of the present invention to the nonparametric case inwhich the underlying distribution is empirically derived, and notnecessarily symmetric. An empirical PDF can be constructed by thefeature space model described herein, resulting in an event-based PDFfor each factor/feature. For each new event, the distribution of theprevious events with respect to each of the geospatial factors in theAOI is empirically approximated. For each factor, the area for which thelikelihood is less than or equal to the likelihood at the new event iscalculated, and the product of such measures is used as the changedetection statistic. Should the change detection statistic exceed aknown or pre-established threshold, the present invention can concludethat the event did not come from the distribution and therefore that asignificant change has been detected.

Changes in a variety of factors can be detected, including buyerbehavior, criminal behavior, and war behavior (e.g., techniques, tacticsand procedures (TTP)), for example, using the change detection methodsof the present invention. Once the present invention analyzes a set ofdata or event-type signature for change detection, it can classify thenew data as being an outlier (meaning that a signature shift hasoccurred), a mixture (meaning there may now be two signatures for theevent-type), or an isolated outlier (meaning not enough outlier eventshave been received in order to categorize it as a signature shift, inwhich case the original signature is kept and no signature shiftresults). With regard to the mixture classification, the presentinvention can operate such that received information pertaining to newevents (e.g., event-type signature) arrives at or passes a threshold forindicating that there is no signature shift, but does not arrive at orpass a threshold for indicating that a full signature change exists. Insuch a case, two signatures can be stored for the given event-type.

As further shown in FIG. 12, an alert component 96 can also be providedwhich incorporates assessments from the event likelihood component 25 inorder to determine and notify individuals or units in the field ofoperation (area of interest) about potential hot spots. Such anotification may include a notice to evacuate the area, or a notice totake certain action in or around the area. In connection with influencecomponent, the alert component can notify field units to move, remove,or insert specific features within an AOI. For example, the field unitcan be notified that certain roadside inspection points need to be movedin order to influence behavior according to the methods described above.Alternatively, the present invention may detect a signature change incertain business, criminal or war behavior, and alert a field team to beprepared to take action (e.g., move location, watch for activity, dongas masks, etc.).

As shown in FIG. 13A, in one embodiment of the invention, the eventlikelihood component 25 is accessible via a network 100 such as theInternet through one or more user input components 101-106. For example,input component 101 can provide a user interface for entering thetraining data used by the event likelihood component in generatingassessments. Layer selection component 102 can be provided with a userinterface to allow a user to select which layers and relevant variablesof interest the user wishes to employ in the analysis. Different layersof the same variable might be selected based on time frame, for example.Alternatively, different variables might be selected within the samelayer. A user might also choose to assign weights to the variablesmanually, or designate the integration of the influence component ofFIG. 12. Area of interest and/or layer update component 103 can beprovided to input new or updated areas of interest and/or layers intothe system. Reverse lookup component 104 can be provided with a userinterface to allow a user to enter information pertaining to an unknownevent type, so as to have the system of the invention determine whichevent type most closely matches the signature corresponding to the inputdata. The reverse lookup component determines the type of eventassociated with the event data by determining an event data signature,comparing the event data signature with known signatures, anddetermining what the most likely event is. Alert units 105 can also beprovided in communication with network 100 to receive alertnotifications as described in connection with FIG. 12.

As shown in FIG. 20, for time sensitive assessments, the invention canproduce a low resolution product (e.g., 201) and then use theinformation from that assessment to triage the remaining processing tocomplete the highest resolution image (e.g., 209). This can occur overmultiple stages of refinement as a background process (e.g., 202-208).As a user requests a result, they receive the most updated assessment ifthe final assessment has not been completed.

While not shown, a result likelihood component can be provided in lieuof or in addition to the event likelihood component, with the differencebeing that the result likelihood component is concerned with pastresults, and not necessarily past events. For example, the resultlikelihood component can collect result information, such as thelocation of the top performing retail store in a retail chain, and usethe system of the present invention to determine a likelihood of asimilar result occurring in a different location.

In one embodiment, the invention can also provide information as to whyan event was predicted for a given geospatial area. For example, if agiven cell appears to have the highest score, designating the closestsignature match, one can drill down into the details of why a given cellor region has the highest score. For example, a user might employ agraphical user interface to select a segment of the display. Theinvention will then reveal a list of all features used in the assessmentsorted according to their contribution to the grid element selected. Thetop ten features are then reported to the user.

In a further embodiment, the invention also contemplates that a givenmap may have “hot” and “cold” cells that vary depending on time of day,time/day of week, time/day of month, or time/day/season of year. It willbe appreciated that the invention can use flat maps and maps adjustedfor the earth's curvature. It will also be appreciated that theinvention allows portability of analysis, as preprocessed data and eventinformation can be stored in a binary file and used in laptops, PDAs,cell phones or other mobile computing devices in the field.

It will be appreciated that various problems presented by governments,private companies and individuals require specific novel solutions foruse with the present invention. For example, a company involved inlandscaping may desire a list of targeted prospects. Such a companywould not be interested in townhomes having no land for trees, shrubs,flowers and the like. Thus, such a company would use the presentinvention with certain static variables of interest programmed in, suchas land utilization (e.g., homes with yards, new building constructionareas) and other similar variables. A general portal access component106, as shown in FIG. 13A, can provide an interface for a user who mayenter general problem specifications, allowing the event likelihoodcomponent 25 to determine which area of interest, layers, variables,weighting and other factors to incorporate in providing an assessment.

Another company type, such as a retail establishment, would likely bemore concerned with past successful results of other similarestablishments (whether its own establishments or those of others) andnot necessarily past events. Such an approach using the presentinvention might assess locations of existing establishments having grossrevenues in excess of a target number, and the invention might determineproximity of such establishments to major and minor roads, stoplights,other complementary retailers (e.g., a shoe store near a dress shop, oran ice cream store near a strip mall), etc. With such information, theinvention would then determine a probability density function or otherappropriate function (e.g., a signature) and compare cells of ageographic grid to determine the location with the highest likelihood ofreproducing the results of the pre-existing retail establishments. Insuch an embodiment, the present invention could map the results from afirst geographic grid to a second grid, such as a developing community,for example.

For law enforcement and similar applications, users of the presentinvention might be more interested with events than results. Also, insuch applications, users might desire to employ specific deliverymechanisms (e.g., binary files delivered by wireless communication) forreal-time information collection and dissemination. In suchapplications, the present invention might also use more dynamicvariables, such as vehicle density during given hours of given days ofthe week. Such information might be helpful in determining potentialget-away routes for would-be criminals, for example.

The present invention can adapt to real-time communication of events inpresenting geospatial analysis and event prediction. For example, thepresent invention may provide different “hot spot” analysis for a seriesof events that occur every few days, as opposed to a batch of eventsthat occurred weeks earlier, followed by another batch of eventsoccurring in current time. In the latter scenario, greater weight can beplaced on the “live” events in predicting where the next similar eventmight occur.

In one embodiment of the present invention, input events can be weightedin any manner desired. In this way, as an example, if more recent eventsare believed to have a higher forecasting impact on future similarevents, recent events can be weighted more times for the resultingassessment. Other factors, such as the lethality of an attack, or actualrevenues, for example, can be considered as factors or events whichreceive greater weight. It will be appreciated that placing greaterweight on a given input event would not require the present invention tomake the same function call and/or determination multiple times. Rather,the function call could be made once, and then the results weightednumerically in order to reduce the memory and computational drag on thesystem.

In one embodiment of the invention, a web-based portal can be providedto allow users to define a problem for which geospatial modeling isdesired. The business features (e.g., payment, contractual, privacy),programming and connectivity features (e.g., Internet programmingavailable by high or low-speed connection through web and applicationservers), and security features (e.g., firewall, SSL) of such anarrangement will be appreciated as well-known in the art.

In a further aspect of the present invention, temporal factors areconsidered in great detail. Database 14 can be adapted to includetemporal data and indexes or indices into the temporal data (in additionto the spatial data and associated spatial data indexes or indices),with search and retrieve capabilities. The search and retrieve functionscan be employed via programming that accepts requests for geospatiallyor temporally-referenced data, accesses the geospatial or temporal datafrom a medium upon which the geospatial or temporal data are stored andprovides responses to the requests. Depending upon a request receivedfrom a user, the programming associated with the geospatial and temporaldatabases can determine whether to access the geospatial database or thetemporal database.

As an example, one index for the geospatial data can be stoplights. Asearch query that involves stoplights and uses the stoplight index termcan thus rule out non-stoplight terms to provide a focused data return.One index for the temporal database can be specific years, such as 2002,for example. A search query that involves the year 2002 and invokes the2002 index can thus rule out non-2002 years.

If, for example, one were studying the effect of putting trafficenforcement cameras at traffic light locations (i.e., stoplightlocations) over a period of years, one could query the database of thepresent invention for data indicating where enforcement cameras wereplaced at different time periods. For example, a query can be run foreach individual year from 2000 to the present for a single jurisdictionor a plurality of geographic locations. The response to such a query canbe a listing of traffic enforcement camera locations, or a map detailinglocations of such camera locations, or a combination of the two.

In a further embodiment of the present invention, events (as opposed totime, place or other temporal or geospatial data) and event-types arestored having event-type data, temporal data and geospatial data, thepresent invention might store “automobile accidents” as event-type data,and further sub-categorize the data according to date, time and locationof the accident. In this way, the user can query the following, forexample: “automobile accidents, occurring during the daytime since 2000at traffic enforcement camera locations within the greater Boston area.”The automobile accidents would be the event-type data, the times wouldbe daytime since 2000 and the geospatial data would be Boston trafficenforcement camera locations. In one embodiment of the presentinvention, a user interface in accordance with the present invention canrespond to such queries with clarification questions, menus or otherinterface options designed to elicit a response from the user to betterenable the search. For example, in the above, the present invention canask the user if a yearly comparison of 2000, 2001, 2002, etc., isdesirable. Further, the present invention can ask the user if it shoulduse daylight approximations based on stored data pertaining to when itwould have been daylight over such time periods (e.g., weather datastorage facilities either provided as part of the database of thepresent invention or as externally accessible, e.g., over the Internetcan be accessed to incorporate the fact that days in the summertime arelonger than days in the winter time, down to the specific minuteaccording to sunrise and sunset data). The results of such a query canbe a report, a map display (e.g., color-coded or otherwisedifferentiated to show differences based on the yearly request).

In one embodiment of the database aspect of the present invention,records can be stored according to a tree-type structure with the actualrecords in locations called leaves. Leaves are end points with nothingbeyond them, and are accessed by going through nodes, which are pointsalong the way. The starting point can be called the root and the numberof access operations required to reach the desired record is called thedepth. As can be imagined, a tree-structured database in accordance withthe present invention can have thousands, millions, or billions ofnodes, leaves, and records. In one embodiment of the present invention,searching algorithms such as a splay tree can be provided to minimizenumber of access operations required to recover desired data recordsover a period of time. In such an embodiment, the tree organizationvaries depending on which nodes are most frequently accessed. In thesplay tree embodiment, the structural change rotating or interchangingthe root with the node in question. One or more other nodes might changeposition as well. In this way, both queries and data additions can bemanaged more efficiently.

In one embodiment of the present invention, the temporal databaseincludes at least one index based upon trends, at least one index basedupon seasons, at least one index based upon cycles, at least one indexbased upon series, at least one index based upon points in time (i.e.,instants), at least one index based upon sequences and at least oneindex based upon intervals. Instants are described by a list of datapoints with a date/time stamp, optionally including attributes, as shownat 350 in FIG. 28 a, for example. Intervals are described as a list ofevents which have start and end date/time stamps, optionally includingattributes, as shown at 355 in FIG. 28 b, for example. Series aredescribed by a series of data values associated with an aggregrate timeinterval (e.g., day, week, month, etc.), as shown at 360 in FIG. 28 c,for example. Cycles can be a sinusoidal function described by afrequency and amplitude as a function of time, as shown at 365 in FIG.28 d, for example. Seasons can be periods of a year, e.g., winter,spring, summer, and fall, or can be selling seasons, for example.Sequences, can be described by an n-step irregular list of date/timestamps, as illustrated at 370 in FIG. 28 e, for example.

Metadata associated with and stored in temporal database can includeidentification information (e.g., creator, description, date/timecreated, status, date range of relevance, keywords (e.g., theme, place),use constraints, etc.), data type information (e.g., instant, interval,series, cycle, sequence, season), recurrence information (e.g., a dataset can have recurrence parameters that describe a repeating pattern(e.g., daily, first Sunday of each month, quarterly, etc.)), quality(accuracy and completeness), temporal reference information (e.g.,reference system definition, calendar system (such as name (e.g., Juliancalendar), parameters (e.g., time zone, daylight savings time))), andattribute data dictionary information, for example.

Various operations can be supported using the temporal databaseincorporated as one aspect of the present invention. For example,instant data can be converted to cycle, interval, sequence and/or seriesdata through analysis. Time series information can be analyzed for aninternal structure, such as a trend, seasonality or autocorrelation, forexample. Data can be plotted to show summary information of temporaldata and analysis, including through a cycle summary chart, a timeseries plot, and a calendar plot, for example. In the calendar plot,different areas may be shaded (e.g., Sundays, the month of February) toindicate heavier occurrences of events being analyzed, for example.

In addition to the above, using the temporal database, the presentinvention can detect changes in event timing, and can further model thelikelihood of a future event occurring at or around a given time in aspecified geospatial area. With regard to detecting changes in eventtiming, the present invention can first establish and store an originaltemporal signature for a given event type based on a determinedlikelihood associating the event type's relative proximity and time ofoccurrence to a variable of interest (VOI) within a geospatial area ofinterest (AOI). For example, if bank robberies tend to occur at theopening of business (e.g., 9 a.m.) in remote bank branches, an originaltemporal signature can depict this. Next, the present invention receivestemporal information pertaining to one or more subsequent events of thegiven event type within the AOI, including timing information for theone or more subsequent events. Thus, for example, two new attempted bankrobberies may have occurred at 9:00 a.m. and 4:30 p.m. Next, the presentinvention classifies the received information as part of the originalsignature or as an outlier, and upon classifying a threshold portion ofthe received information as an outlier, the present invention canestablish and store a reassessed signature for the given event type.

With regard to modeling the likelihood of a future event occurring at ornear a given time in a specified geospatial area, the present inventionprovides programming capable of making probability assessments of agiven event-type for a given geospatial area of interest (AOI) based onthe geospatial characteristics of at least one variable of interest(VOI) and past geospatial and temporal data for the event-type, asdescribed above. Next, the present invention determines, using influencecomponent, for example, whether the at least one VOI is modifiable andcausal of the event-type within a pre-established time range. Then, upondetermining that the at least one VOI is modifiable and causal of theevent-type within a pre-established time range, the present inventioniteratively executes the programming while modifying a quantitativecharacteristic of the VOI until the desired likelihood of the event typeis obtained.

It will be appreciated that the present invention can incorporate atemporal data operating standard or format that includes a cyclicredundancy check value for detecting data corruption. The presentinvention can further incorporate a chron file format with supportingsoftware library for conversion of data, input/output and metadata.

The geospatial modeling method and system of the present invention hasan extensive array of applications. Service and product-based businessescan learn where to target market, to whom to market, where to locate anestablishment, what markets to expand into, what variables are morereliable predictors of success, and so forth.

Example 1

The following example is provided as an illustrative case of theoperation of the present invention.

User A is a metropolitan crime analyst who develops planning maps forthe tasking of law enforcement units. Due to a recent increase inbreaking and entering offenses, User A desires to create a summary mapthat will describe the likelihood of a breaking and entering eventoccurring throughout her city.

FIG. 14 is an example geographical depiction of an area of interest 140within User A's city, available through the boundary component of thepresent invention. As noted by elements 141 in FIG. 15, User A can applytraining data from the previous month's breaking and entering offensesonto the area of interest, using training data input component 30. Asshown in FIG. 16, User A can then import relevant base layer data fromher city as available through layer component. The layers included are“roads” 142, “rivers” 143, “cul-de-sacs” 144, and “street lamps” 145.

User A can then use the boundary component of the invention to create auniform grid 147 over the city or area of interest 140, as shown in FIG.17. Next, User A uses the signature derivation component of the presentinvention to create a signature of breaking and entering events. In oneembodiment, this signature is selected to be a collection of probabilitydensity functions for the distances to the nearest of each of thefeatures.

For an event i, an estimate of the density at i for the distance toroads is given by:

${\hat{f}}_{i} = {\sum\limits_{j = 1}^{n}{\frac{1}{n\sqrt{2\pi\;\sigma^{2}}}\exp\left\{ \frac{- \left( {{dist}\mspace{14mu}{from}\mspace{14mu}{event}\mspace{14mu} j\mspace{14mu}{to}\mspace{14mu}{nearest}\mspace{14mu}{road}} \right)^{2}}{2\sigma^{2}} \right\}}}$Where  j  denotes  the  j^(th)  event  training  point  where  j = {1…  n}

User A next uses the event likelihood component of the present inventionto link the signature of breaking and entering with the regular grid. Inone embodiment, the regular grid has been previously analyzed using thefunction component of the present invention, such that values orfunctional measurements associated with each cell of the grid have beenidentified and indexed. For example, a nearest neighbor distancemeasurement from each cell's center point to the variable of interest(or a feature within the variable of interest, e.g.) will have beendetermined and stored via the function component. The linking of thederived signature with the regular grid can be performed, in oneembodiment, by calculating the distance from each event from thetraining data to the nearest feature element, and assessing thelikelihood score associated with that distance for each feature. In oneembodiment, a weighted average of these scores is taken to find thecombined score across all features.

As shown in FIG. 18, User A can then use the assessment element of theinvention to shade the resulting assessment according to the likelihoodscore. In one embodiment, areas of deeper shading such as 148 a and 148b, for example, can be used to denote a greater likelihood of such anevent occurring in or around that location in the future. On the otherhand, areas of lighter or no shading such as 149 can indicate areaswhere the event is determined to be less likely to occur.

Example 2

The following example is provided as an additional illustrative case ofthe operation of the present invention.

User B is a planning district manager deciding on the best location fora new municipal fire station. There are twenty-two possibleneighborhoods in the city where a fire station could be located. User Bwould like to recommend a location that will place the new fire stationin the area most likely to have fire incidents causing a significantamount of damage based on observed data for the previous year. Thetravel time for an emergency vehicle to the fire location is the primarymeasure of distance for this task. Data for city locations is stored ina parcel map outlining each identified parcel in the community; theseparcels will be used as the output grid for the analysis.

As shown in FIG. 21, User B applies training data 210 from the previousyear's house fire incidents. In this example, User B uses the inventionto import relevant base layer data from her city. The layers caninclude, for example “roads” 211, “neighborhoods” 212, “current firestation 213”, “housing construction type”, and “housing values”, asshown in FIG. 22.

User B uses the invention to select the city parcel layer 215 as theoutput layer, as shown in FIG. 23. This layer 215 is an irregular gridof shapes 216 (see FIGS. 21 and 22) that define the boundaries oftaxable property units. They are useful because they containconstruction and value data on the structures that are built on thoseparcels.

User B can then use the invention to create a signature of residentialfire events. This signature is selected to be a collection ofprobability density functions for the distances to the nearest of eachof the features. As an example, the housing construction variable maycontain the following proportions:

Stone: 0.20

Brick: 0.20

Wood: 0.60

User B next uses the histogram tool of the present invention to create amanual density function for construction type. FIG. 24A shows ahistogram 220 and FIG. 24B shows a triangular distribution (i.e., withmin, max, mode). User B then uses the invention to link the signature ofhigh damage house fires with the irregular grid. This linkage isperformed by calculating the travel distance along the road network fromeach parcel grid cell to the nearest feature element and finding thelikelihood score associated with that distance for each feature. In oneembodiment of the invention as described above, a weighted average ofthese scores can be taken to find the combined score across allfeatures. User B then uses the invention to shade the resultingassessment according to the likelihood score, as shown in the diagram225 of FIG. 25. Upon receiving the final assessment, User B can see thatneighborhood P is the best location to place the firehouse based on thehigh vulnerability of houses in that area. User B can also send an alertmessage using the invention to the fire personnel who operate in thatneighborhood. It will be appreciated that alert notifications can alsobe employed in real-time emergency situations.

Example 3

The following example is provided as an additional illustrative case ofthe operation of the present invention.

User C is a shopping mall designer/manager. The flow of shoppers throughthe facility and their shopping habits is of interest to User C. Ccreates a spatial assessment of shopping for electronics making use ofthe invention. User C realizes that there is far too much trafficconcentrated at the south entrance to the shopping mall and wishes tomove a portion of the traffic to the north entrance area. User Cdetermines that he can modify the locations of valet parking stations,ATM machines, and mall security stations. By examining theattraction-avoidance index for each feature, User C discovers that ATMsand valet parking are attractor features, while security stations arerepellors. User C uses the Spatial Behavior Modification tool to findthe best locations for the available features, and determines locationsfor the ATMs, valet parking, and security stations that equalize trafficbetween the two entrances without reducing the total shopping traffic atthe mall.

It will be apparent to one skilled in the art that any computer systemthat includes suitable programming means for operating in accordancewith the disclosed methods also falls well within the scope of thepresent invention. Suitable programming means include any means fordirecting a computer system to execute the steps of the system andmethod of the invention, including for example, systems comprised ofprocessing units and arithmetic-logic circuits coupled to computermemory, which systems have the capability of storing in computer memory,which computer memory includes electronic circuits configured to storedata and program instructions, programmed steps of the method of theinvention for execution by a processing unit. The invention also may beembodied in a computer program product, such as a diskette or otherrecording medium, for use with any suitable data processing system. Thepresent invention can further run on a variety of platforms, includingMicrosoft Windows™, Linux™, Sun Solaris™, HP/UX™, IBM AIX™ and Javacompliant platforms, for example.

The invention may be embodied in other specific forms without departingfrom the spirit or essential characteristics thereof. The presentembodiments are therefore to be considered in all respects asillustrative and not restrictive, the scope of the invention beingindicated by the claims of the application rather than by the foregoingdescription, and all changes which come within the meaning and range ofequivalency of the claims are therefore intended to be embraced therein.

1. A computer-implemented geospatial and temporal data storage system,comprising: a geospatial database including geospatial data and aplurality of indices into the geospatial data; a temporal databaseincluding temporal data and a plurality of indices into the temporaldata; and means for searching for and retrieving data from thegeospatial and temporal databases.
 2. The data storage system of claim 1wherein the means for searching for and retrieving data includesprogramming that accepts requests for geospatially ortemporally-referenced data, accesses the geospatial or temporal datafrom a medium upon which the geospatial or temporal data are stored andprovides responses to the requests.
 3. The system of claim 1 furtherincluding computer programming associated with the geospatial andtemporal databases for determining whether to access the geospatialdatabase or the temporal database depending upon a received request. 4.The system of claim 1 wherein the temporal database includes at leastone index based upon trends.
 5. The system of claim 1 wherein thetemporal database includes at least one index based upon seasons.
 6. Thesystem of claim 1 wherein the temporal database includes at least oneindex based upon cycles.
 7. The system of claim 1 wherein the temporaldatabase includes at least one index based upon sequences.
 8. The systemof claim 1 wherein the temporal database includes at least one indexbased upon intervals.
 9. The system of claim 1 further including anevent-type database including event-type data and a plurality of indicesinto the event-type data, wherein the means for searching for andretrieving data from the temporal database includes an interface thataccepts queries for event-type data.
 10. The system of claim 9 wherein aplurality of events are stored, each having one or more elements ofevent-type data, temporal data and geospatial data.
 11. A computerizedmethod for detecting changes in event-type timing, comprising the stepsof: establishing and storing, in a geospatial and temporal data storagesystem, an original temporal signature for a given event type based on adetermined likelihood associating the event type's relative proximityand time of occurrence to a variable of interest (VOI) within ageospatial area of interest (AOI); receiving temporal informationpertaining to one or more subsequent events of the given event typewithin the AOI, including timing information for the one or moresubsequent events; classifying the received information as part of theoriginal signature or as an outlier; and upon the received informationmeeting or exceeding a pre-determined threshold for classification as anoutlier, establishing and storing a reassessed signature for the givenevent type.
 12. A computer-implemented method for providing anevent-type storage system having geospatial and temporal elements,comprising: providing an event-type database having event-type data,temporal data and geospatial data, and a plurality of indices into theevent-type data, temporal data and geospatial data; and providing meansfor searching for and retrieving data from the event-type, geospatialand temporal databases.
 13. The method of claim 12 wherein the means forsearching for and retrieving data includes programming that acceptsrequests for event-type, geospatially or temporally-referenced data,accesses the event-type, geospatial or temporal data from a medium uponwhich the event-type, geospatial or temporal data are stored andprovides responses to the requests.
 14. The method of claim 12 furtherincluding the step of providing computer programming associated with theevent-type, geospatial and temporal databases for determining whether toaccess the event-type database, geospatial database or the temporaldatabase depending upon a received request.
 15. The method of claim 12wherein the temporal database includes at least one index based upontrends.
 16. The method of claim 12 wherein the temporal databaseincludes at least one index based upon seasons.
 17. The method of claim12 wherein the temporal database includes at least one index based uponcycles.
 18. The method of claim 12 wherein the temporal databaseincludes at least one index based upon sequences.
 19. The method ofclaim 12 wherein the temporal database includes at least one index basedupon intervals.